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Author: Zhixiong Pan
When we talk about AI, the public opinion arena can easily be distracted by topics such as 'parameter scale', 'ranking positions', or 'which new model has crushed whom'. We cannot say that these noises are meaningless, but they often act like a layer of froth, obscuring the more essential undercurrents beneath the surface: a secret war over the distribution of AI rights is quietly unfolding in today's technological landscape.
If you raise the perspective to the scale of civilizational infrastructure, you will find that artificial intelligence is simultaneously presenting two entirely different yet intertwined forms.
A type of 'lighthouse' like the one hanging high above the coast, controlled by a few giants, pursuing the farthest illumination distance, representing the cognitive limits humans can currently touch.
Another type of 'torch' that can be held in hand, it pursues portability, privacy, and replicability, representing the intelligent baseline accessible to the public.
Understanding these two types of light allows us to escape the marketing jargon trap and clearly judge where AI will take us, who will be illuminated, and who will be left in the dark.
Lighthouse: The cognitive height defined by SOTA.
The so-called 'lighthouse' refers to models at the Frontier / SOTA (State of the Art) level. In dimensions such as complex reasoning, multimodal understanding, long-chain planning, and scientific exploration, they represent the strongest capabilities, highest costs, and most centralized organizations.
Institutions like OpenAI, Google, Anthropic, xAI are typical 'tower builders'; what they construct is not just a series of model names, but a production method of 'exchanging extreme scale for boundary breakthroughs.'
Why the lighthouse is destined to be a game for a few.
The training and iteration of cutting-edge models essentially forcibly bundle three extremely scarce resources together.
First is computing power, which not only implies expensive chips but also high-cardinality clusters, long training windows, and extremely high interconnection network costs; secondly, data and feedback require the cleaning of massive corpora, as well as continuously iterating preference data, complex evaluation systems, and intense manual feedback; finally, there are engineering systems that encompass distributed training, fault-tolerant scheduling, inference acceleration, and the entire pipeline for converting research results into usable products.
These elements create a very high threshold; it cannot be replaced by a few geniuses writing 'smarter code.' It is more like a vast industrial system, capital-intensive, with a complex chain, and marginal improvements becoming increasingly expensive.
Therefore, lighthouses naturally carry centralized characteristics: they are often controlled by a few institutions that possess training capabilities and data loops, ultimately being used by society in the form of APIs, subscriptions, or closed products.
The dual significance of the lighthouse: breakthrough and traction.
The existence of the lighthouse is not to 'make everyone write copy faster'; its value lies in two more hardcore functions.
First is the exploration of cognitive limits. When tasks approach the edge of human capabilities, such as generating complex scientific hypotheses, conducting interdisciplinary reasoning, multimodal perception and control, or long-range planning, what you need is the strongest beam of light. It does not guarantee absolute correctness, but it can shine a light on the 'feasible next step' further.
Secondly, there is traction in the technological route. Cutting-edge systems often first run through new paradigms: whether it’s better alignment methods, more flexible tool invocation, or more robust reasoning frameworks and security strategies. Even if they are later simplified, distilled, or open-sourced, the initial paths are often opened up by the lighthouse. In other words, the lighthouse is a societal-level laboratory that allows us to see 'how far intelligence can reach' and forces the entire industry chain to improve efficiency.
The shadows of the lighthouse: dependence and single-point risks.
But the lighthouse also has obvious shadows, and these risks are often not written in product launch events.
The most direct issue is controlled accessibility. What you can use and whether you can afford it entirely depends on the provider's strategy and pricing. This leads to a high dependency on platforms: When intelligence primarily exists as cloud services, individuals and organizations effectively outsource critical capabilities to the platform.
Behind convenience lies fragility: disconnection, service suspension, policy changes, price increases, and interface changes can all instantly render your workflow ineffective.
The deeper hidden danger lies in privacy and data sovereignty. Even with compliance and commitments, the flow of data itself remains a structural risk. Especially in scenarios involving healthcare, finance, government, and core corporate knowledge, 'putting internal knowledge on the cloud' is often not merely a technical issue, but a serious governance issue.
Moreover, as more industries hand over critical decision-making processes to a few model providers, systemic biases, evaluation blind spots, adversarial attacks, and even supply chain disruptions will be magnified into significant social risks. The lighthouse can illuminate the sea, but it belongs to a part of the coastline: it provides direction but also implicitly defines the shipping lanes.
Torch: the baseline of intelligence defined by open source.
Shifting focus from the distance, you will see another light source: an ecosystem of models that are open-source and can be deployed locally. DeepSeek, Qwen, Mistral, etc. are just some of the more prominent representatives. Behind them represents a new paradigm that transforms quite strong intelligent capabilities from 'cloud-scarce services' into 'downloadable, deployable, and modifiable tools.'
This is the 'torch.' What it corresponds to is not the upper limit of capability, but the baseline. This does not mean 'low capability,' but represents the intelligent baseline that the public can access unconditionally.
The significance of the torch: turning intelligence into an asset.
The core value of the torch lies in transforming intelligence from a rental service into an owned asset, reflected in three dimensions: privately owned, transferable, and composable.
What is meant by 'privately owned' is that model weights and inference capabilities can run locally, on an intranet, or proprietary cloud. 'I have an intelligent system that works' is fundamentally different from 'I am renting the intelligence of a certain company.'
Transferability means you can switch freely between different hardware, environments, and vendors without binding critical capabilities to a single API.
Composable means you can combine models with retrieval (RAG), fine-tuning, knowledge bases, rule engines, and permission systems to form a system that fits your business constraints rather than being boxed in by some generic product.
This translates into very concrete scenarios in reality. Internal knowledge Q&A and process automation in enterprises often require strict permissions, audits, and physical isolation; regulated industries such as healthcare, government, and finance have strict 'data cannot leave the domain' red lines; while in manufacturing, energy, and on-site operations in weak network or offline environments, edge-side reasoning is a necessity.
For individuals, long-term accumulated notes, emails, and private information also require a local intelligent agent to manage, rather than entrusting a lifetime of data to some 'free service.'
The torch makes intelligence no longer just about access rights, but more like a means of production: you can build tools, processes, and safeguards around it.
Why the torch will shine brighter and brighter.
The improvement of open-source model capabilities is not accidental, but comes from the confluence of two paths. One is the diffusion of research; cutting-edge papers, training techniques, and reasoning paradigms are quickly absorbed and replicated by the community; the other is the ultimate optimization of engineering efficiency, such as quantization (like 8-bit/4-bit), distillation, inference acceleration, layered routing, and MoE (Mixture of Experts) technologies, which continuously drive 'usable intelligence' down to cheaper hardware and lower deployment thresholds.
Thus, a very realistic trend emerges: the strongest model determines the ceiling, but the 'sufficiently strong' model determines the speed of popularization. In most tasks of social life, it is not necessary to have the 'strongest'; what is needed is 'reliable, controllable, and stable in cost.' The torch happens to correspond to this kind of demand.
The cost of the torch: security is outsourced to the user.
Of course, the torch is not inherently just; its cost is the transfer of responsibility. Many risks and engineering burdens that were originally borne by platforms are now transferred to users.
The more open a model is, the more likely it is to be used to generate scam scripts, malicious code, or deepfakes. Open source does not equate to harmless; it merely decentralizes control while also decentralizing responsibility. Additionally, local deployment means you must solve a series of issues yourself, including evaluation, monitoring, prompt injection protection, permission isolation, data de-identification, model updates, and rollback strategies.
In fact, many so-called 'open source' are more accurately described as 'open weights,' still constrained in terms of commercial use and redistribution; this is not only a moral issue, but also a compliance issue. The torch gives you freedom, but freedom is never 'zero-cost.' It is more like a tool: it can build, but it can also harm; it can save itself, but it also requires training.
The convergence of light: the co-evolution of upper limits and baselines.
If we only view the lighthouse and torch as 'giants vs. open source' opposition, we will miss a more realistic structure: they are two segments of the same technological river.
The lighthouse is responsible for pushing boundaries, providing new methodologies and paradigms; the torch is responsible for compressing, engineering, and sinking these achievements to make them a widely applicable productivity tool. This diffusion chain is already very clear today: from papers to replication, from distillation to quantization, and finally to local deployment and industry customization, ultimately achieving an overall elevation of the baseline.
The elevation of the baseline will in turn affect the lighthouse. When a 'sufficiently strong baseline' is accessible to everyone, it becomes difficult for giants to maintain a monopoly based solely on 'basic capabilities' and they must continue to invest resources to seek breakthroughs. Meanwhile, the open-source ecosystem will generate richer evaluations, adversarial feedback, and usage feedback, which in turn promotes frontier systems to be more stable and controllable. A large number of innovative applications occur within the torch ecosystem; the lighthouse provides capability, while the torch provides the soil.
Thus, rather than seeing this as two camps, it is better to say this is two types of institutional arrangements: one system concentrates extreme costs to achieve breakthroughs at the upper limit; the other system disperses capabilities to exchange for popularization, resilience, and sovereignty. Both are indispensable.
Without a lighthouse, technology easily falls into the stagnation of 'only capable of cost-performance optimization'; without a torch, society easily falls into the dependency of 'capabilities being monopolized by a few platforms.'
The more difficult but crucial part: What exactly are we fighting for?
The competition between lighthouses and torches superficially revolves around the differences in model capabilities and open-source strategies, but in reality, it is a secret war about the distribution of AI rights. This war does not take place on a battlefield filled with smoke but unfolds in three seemingly calm yet decisive dimensions for the future:
First, the contest over the definition of 'default intelligence.' When intelligence becomes infrastructure, the 'default option' means power. Who provides the default? Whose values and boundaries does the default follow? What are the reviews, preferences, and commercial incentives of the default? These questions do not automatically disappear because the technology is stronger.
Second, the way of bearing externalities is contested. Training and inference consume energy and computing power; data collection involves copyright, privacy, and labor; model outputs influence public opinion, education, and employment. Both lighthouses and torches create externalities, but the distribution methods differ: lighthouses are more centralized, can be regulated, but are more like single points; torches are more decentralized, more resilient, but harder to govern.
Third, the position of individuals in the system is contested. If all important tools must be 'online, logged in, paid for, and compliant with platform rules,' an individual's digital life will become like renting: convenient, but never truly theirs. The torch offers another possibility: allowing people to possess a part of 'offline capabilities' and keep control of privacy, knowledge, and workflows in their own hands.
A dual-track strategy will become the norm.
In the foreseeable future, the most reasonable state is not 'fully closed source' or 'fully open source,' but rather a combination similar to that of an electrical system.
We need lighthouses for extreme tasks to handle scenarios requiring the strongest reasoning, cutting-edge multimodal capabilities, cross-domain exploration, and complex research assistance; we also need torches for key assets to build defenses in scenarios involving privacy, compliance, core knowledge, long-term stable costs, and offline availability. Between the two, a large number of 'intermediate layers' will emerge: proprietary models built by enterprises, industry models, distilled versions, and mixed routing strategies (simple tasks handled locally, complex tasks handled in the cloud).
This is not a compromise but an engineering reality: the upper limit seeks breakthroughs, while the baseline seeks popularization; one pursues extremity, while the other pursues reliability.
Conclusion: The lighthouse guides the distance, the torch guards what is beneath our feet.
The lighthouse determines how high we can push intelligence; that is civilization's offense in the face of the unknown.
The torch determines how widely we can distribute intelligence; that is society's self-restraint in the face of power.
Applauding breakthroughs in SOTA is reasonable, as it expands the boundaries of what humans can think about; applauding the iteration of open-source and privatization is equally reasonable, as it enables intelligence to belong not just to a few platforms, but to become tools and assets for more people.
The true watershed of the AI era may not be 'whose model is stronger,' but rather whether you have a beam of light that does not need to be borrowed from anyone when night falls.
